package net.bwie.realtime.jtp.dwd.log.job;


import com.alibaba.fastjson.JSON;
import net.bwie.realtime.jtp.dwd.log.function.AdjustIsNewProcessFunction;
import net.bwie.realtime.jtp.dwd.log.function.AppLogSplitProcessFunction;
import net.bwie.realtime.jtp.utils.KafkaUtil;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SideOutputDataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;


/**
 *  * DWD层数据应用开发：将ODS层采集原始日志数据，进行分类处理，存储Kafka队列。
 *  *      数据流向：kafka -> flink datastream -> kafka
 */
public class JtpAppLogEtlJob {

    public static void main(String[] args) throws Exception{

        // 1.执行环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 2.数据源-source
        DataStream<String> kafkaDataStream = KafkaUtil.consumerKafka(env, "topic-log");


        // 3.数据转换-transformation
        DataStream<String> pageStream = processLog(kafkaDataStream);
        pageStream.print("kafka");

        // 4.数据输出-sink
        KafkaUtil.producerKafka(pageStream,"dwd-traffic-page-log");


        // 5.触发执行-execute
        env.execute("JtpAppLogEtlJob");
    }
    /**
     * 对实时获取APP流量日志数据进行ETL处理，并且进行分流，存储Kafka消息队列
     *      1-数据清洗
     *      2-新老访客状态标记修复
     *      3-数据分流
     *      4-存储数据
     * @param stream app日志流
     */
    static DataStream<String> processLog(DataStream<String> stream) {
        // 1. 数据清洗
        DataStream<String> jsonStream = AppLogCleaned(stream);

        // 2. 新老访客状态标记修复
        DataStream<String> etlStream = processIsNew(jsonStream);

        // 3. 数据分流
        DataStream<String> pageStream = splitStream(etlStream);

        return pageStream;
    }

    /**
     * 3.日志数据分流
     * @param stream
     * @return
     */
    private static DataStream<String> splitStream(DataStream<String> stream) {
        // todo 1.侧边流输出标记
        final OutputTag<String> errorTag = new OutputTag<String>("error-log"){};
        final OutputTag<String> startTag = new OutputTag<String>("start-log"){};
        final OutputTag<String> displayTag = new OutputTag<String>("display-log"){};
        final OutputTag<String> actionTag = new OutputTag<String>("action-log"){};

        // todo 2.日志分流处理
        SingleOutputStreamOperator<String> pageStream = stream.process(
                new AppLogSplitProcessFunction(errorTag, startTag, displayTag, actionTag)
        );

        // todo 3.侧流输出
        SideOutputDataStream<String> errorStream = pageStream.getSideOutput(errorTag);
        KafkaUtil.producerKafka(errorStream,"dwd-traffic-error-log");
        SideOutputDataStream<String> startStream = pageStream.getSideOutput(startTag);
        KafkaUtil.producerKafka(startStream,"dwd-traffic-start-log");
        SideOutputDataStream<String> displayStream = pageStream.getSideOutput(displayTag);
        KafkaUtil.producerKafka(displayStream,"dwd-traffic-display-log");
        SideOutputDataStream<String> actionStream = pageStream.getSideOutput(actionTag);
        KafkaUtil.producerKafka(actionStream,"dwd-traffic-action-log");


        // todo 4.输出主流
        return pageStream;
    }

    /**
     * 2.新老访客状态标记修复
     * @param stream
     * @return
     */
    private static DataStream<String> processIsNew(DataStream<String> stream) {
        //a.按照设备id进行分组
        KeyedStream<String, String> midStream = stream.keyBy(new KeySelector<String, String>() {
            @Override
            public String getKey(String value) throws Exception {
                return JSON.parseObject(value).getJSONObject("common").getString("mid");
            }
        });

        // b.状态编程，对is_new校验修复
        DataStream<String> isNewStream =  midStream.process(new AdjustIsNewProcessFunction());

        // c.返回数据流
        return isNewStream;
    }

    /**
     * 1.数据清洗,将不合格数据侧边输出
     * @param stream
     * @return
     */
    private static DataStream<String> AppLogCleaned(DataStream<String> stream) {
        //a-脏数据侧边流输出标记
        final OutputTag<String> dirtyTag = new OutputTag<String>("dirty-log"){};

        //b.数据清洗处理
        SingleOutputStreamOperator<String> cleanedStream = stream.process(new ProcessFunction<String, String>() {
            @Override
            public void processElement(String value, Context ctx, Collector<String> out) throws Exception {
                try {
                    // a.解析json数据
                    JSON.parseObject(value);
                    // b.没有异常，解析正确，正常输出
                    out.collect(value);
                }catch (Exception e){
                    // c.捕获异常，侧边流输出数据
                    ctx.output(dirtyTag,value);
                }
            }
        });

        //c.侧边流输出:脏数据
        DataStream<String> dirtyStream = cleanedStream.getSideOutput(dirtyTag);
        KafkaUtil.producerKafka(dirtyStream,"dwd-traffic-dirty-log");

        //d.返回正常数据
        return cleanedStream;
    }
}
